Abstract LB061: Proteomic-based stemness score measure oncogenic dedifferentiation and enable the identification of druggable targets
Authors: Tathiane M. Malta, Iga Kołodziejczak, Renan Simões, Antonio Colaprico, Erik Storrs, Francesca Petralia, Felipe da v Leprevost, Rossana L. Segura, Elizabeth Demicco, Alexander J. Lazar, Weiping Ma, Pietro Pugliese, Michele Ceccarelli, Bozena Kamińska, Alexey I. Nesvizhski, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Li Ding, Maciej Wiznerowicz
Journal: Cancer Research (2023)

Abstract Progression and therapeutic resistance in cancer have been strongly associated with the acquisition of a stemness phenotype. Here, we provide new stemness indices for assessing the degree of oncogenic dedifferentiation in tumor samples. We used a machine learning model to predict the stemness molecular phenotype based on proteomic data. The prediction model was built from human pluripotent stem cell from the Human Induced Pluripotent Stem Cells Consortium (HipSci) and applied to compute stemness indices on the Clinical Proteomic Tumor Analysis Consortium (CPTAC) tumor samples, consisting in their proteogenomic hallmarks of stemness. The obtained stemness scores based on protein expression are novel and original, and are significantly more robust compared to our previous published work. The obtained proteomic score is able to classify stem cells and non-stem cell classes. The initial analysis of over 2000 tumor samples obtained from twelve types of primary carcinomas of breast, ovary, lung, kidney, uterus, brain (pediatric and adult), head and neck, liver, stomach, colon, and pancreas has confirmed our previously published results. Indexing of CPTAC tumors with proteomic stemness score brought us with previously unappreciated findings. We integrated the stemness scores computed using proteins with gene expression, DNA methylation, microRNA, copy number alteration and protein post-translational modification to identify coherent proteogenomic stemness association. Our initial findings identified proteins and phospho-proteins as active nodes of signaling pathways and transcriptional networks that drive aggressiveness of the primary tumors that cause resistance to existing therapies. The correlation between stemness scores and protein expression resulted in the identification of potential drug targets for anti-cancer therapy both tumor-specific and shared among different tumor types. Our results also revealed stemness-associated proteins predictive of clinical outcome across analyzed tumor types. Finally, we validated some stemness targets by immunohistochemistry in independent samples and confirmed the association with clinical outcome. Targeting the proteins here identified and cellular mechanisms that drive a stemness phenotype with existing or novel drugs may eventually lead for clinical development of effective cures for cancer patients. Citation Format: Tathiane M. Malta, Iga Kołodziejczak, Renan Simões, Antonio Colaprico, Erik Storrs, Francesca Petralia, Felipe da v Leprevost, Rossana L. Segura, Elizabeth Demicco, Alexander J. Lazar, Weiping Ma, Pietro Pugliese, Michele Ceccarelli, Bozena Kamińska, Alexey I. Nesvizhski, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Maciej Wiznerowicz. Proteomic-based stemness score measure oncogenic dedifferentiation and enable the identification of druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB061.